import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ = "cpu" ,  lowerCAmelCase__ = "openai/clip-vit-large-patch14"		)	->   None:

							'''simple docstring'''


							__lowercase					     =				device
							__lowercase					     =				CLIPTokenizerFast.from_pretrained(lowerCAmelCase__		)
							__lowercase					     =				[0.4814_5466, 0.457_8275, 0.4082_1073]
							__lowercase					     =				[0.2686_2954, 0.2613_0258, 0.2757_7711]
							__lowercase					     =				torchvision.transforms.Normalize(self.image_mean ,  self.image_std		)
							__lowercase					     =				torchvision.transforms.Resize(2_24		)
							__lowercase					     =				torchvision.transforms.CenterCrop(2_24		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.resize(lowerCAmelCase__		)
							__lowercase					     =				self.center_crop(lowerCAmelCase__		)
							__lowercase					     =				self.normalize(lowerCAmelCase__		)
							return images





			def __call__(  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  **lowerCAmelCase__		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				self.tokenizer(text=lowerCAmelCase__ ,  **lowerCAmelCase__		)
							__lowercase					     =				self.preprocess_img(lowerCAmelCase__		)
							__lowercase					     =				{key: value.to(self.device		) for (key, value) in encoding.items()}
							return encoding




class   _UpperCamelCase (		nn.Module					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=0.01 ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=False ,  lowerCAmelCase__=True ,  lowerCAmelCase__="image" ,  lowerCAmelCase__=True ,  lowerCAmelCase__=False ,  lowerCAmelCase__=False ,  lowerCAmelCase__=False ,  )	->   None:

							'''simple docstring'''


							super().__init__()
							__lowercase					     =				None
							__lowercase					     =				device if device else get_device()
							if vqgan:
											__lowercase					     =				vqgan
							else:
											__lowercase					     =				load_vqgan(self.device ,  conf_path=lowerCAmelCase__ ,  ckpt_path=lowerCAmelCase__		)
							self.vqgan.eval()
							if clip:
											__lowercase					     =				clip
							else:
											__lowercase					     =				CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''		)
							self.clip.to(self.device		)
							__lowercase					     =				ProcessorGradientFlow(device=self.device		)

							__lowercase					     =				iterations
							__lowercase					     =				lr
							__lowercase					     =				log
							__lowercase					     =				make_grid
							__lowercase					     =				return_val
							__lowercase					     =				quantize
							__lowercase					     =				self.vqgan.decoder.z_shape





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=5 ,  lowerCAmelCase__=True		)	->   str:

							'''simple docstring'''


							__lowercase					     =				[]
							if output_path is None:
											__lowercase					     =				'''./animation.gif'''
							if input_path is None:
											__lowercase					     =				self.save_path
							__lowercase					     =				sorted(glob(input_path + '''/*'''		)		)
							if not len(lowerCAmelCase__		):
											raise ValueError(
											    '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
											    ''' function?)'''		)
							if len(lowerCAmelCase__		) == 1:
											print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''		)
							__lowercase					     =				total_duration / len(lowerCAmelCase__		)
							__lowercase					     =				[frame_duration] * len(lowerCAmelCase__		)
							if extend_frames:
											__lowercase					     =				1.5
											__lowercase					     =				3
							for file_name in paths:
											if file_name.endswith('''.png'''		):
															images.append(imageio.imread(lowerCAmelCase__		)		)
							imageio.mimsave(lowerCAmelCase__ ,  lowerCAmelCase__ ,  duration=lowerCAmelCase__		)
							print(F"gif saved to {output_path}"		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None		)	->   Optional[int]:

							'''simple docstring'''


							if not (path or img):
											raise ValueError('''Input either path or tensor'''		)
							if img is not None:
											raise NotImplementedError
							__lowercase					     =				preprocess(Image.open(lowerCAmelCase__		) ,  target_image_size=2_56		).to(self.device		)
							__lowercase					     =				preprocess_vqgan(lowerCAmelCase__		)
							__lowercase     ,	*__lowercase					     =				self.vqgan.encode(lowerCAmelCase__		)
							return z





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.latent.detach().requires_grad_()
							__lowercase					     =				base_latent + transform_vector
							if self.quantize:
											__lowercase     ,	*__lowercase					     =				self.vqgan.quantize(lowerCAmelCase__		)
							else:
											__lowercase					     =				trans_latent
							return self.vqgan.decode(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__=None		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.clip_preprocessor(text=lowerCAmelCase__ ,  images=lowerCAmelCase__ ,  return_tensors='''pt''' ,  padding=lowerCAmelCase__		)
							__lowercase					     =				self.clip(**lowerCAmelCase__		)
							__lowercase					     =				clip_outputs.logits_per_image
							if weights is not None:
											__lowercase					     =				similarity_logits * weights
							return similarity_logits.sum()





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				self._get_clip_similarity(pos_prompts['''prompts'''] ,  lowerCAmelCase__ ,  weights=(1 / pos_prompts['''weights'''])		)
							if neg_prompts:
											__lowercase					     =				self._get_clip_similarity(neg_prompts['''prompts'''] ,  lowerCAmelCase__ ,  weights=neg_prompts['''weights''']		)
							else:
											__lowercase					     =				torch.tensor([1] ,  device=self.device		)
							__lowercase					     =				-torch.log(lowerCAmelCase__		) + torch.log(lowerCAmelCase__		)
							return loss





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				torch.randn_like(self.latent ,  requires_grad=lowerCAmelCase__ ,  device=self.device		)
							__lowercase					     =				torch.optim.Adam([vector] ,  lr=self.lr		)

							for i in range(self.iterations		):
											optim.zero_grad()
											__lowercase					     =				self._add_vector(lowerCAmelCase__		)
											__lowercase					     =				loop_post_process(lowerCAmelCase__		)
											__lowercase					     =				self._get_CLIP_loss(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
											print('''CLIP loss''' ,  lowerCAmelCase__		)
											if self.log:
															wandb.log({'''CLIP Loss''': clip_loss}		)
											clip_loss.backward(retain_graph=lowerCAmelCase__		)
											optim.step()
											if self.return_val == "image":
															yield custom_to_pil(transformed_img[0]		)
											else:
															yield vector





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							wandb.init(reinit=lowerCAmelCase__ ,  project='''face-editor'''		)
							wandb.config.update({'''Positive Prompts''': positive_prompts}		)
							wandb.config.update({'''Negative Prompts''': negative_prompts}		)
							wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}		)
							if image_path:
											__lowercase					     =				Image.open(lowerCAmelCase__		)
											__lowercase					     =				image.resize((2_56, 2_56)		)
											wandb.log('''Original Image''' ,  wandb.Image(lowerCAmelCase__		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							if not prompts:
											return []
							__lowercase					     =				[]
							__lowercase					     =				[]
							if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				[prompt.strip() for prompt in prompts.split('''|'''		)]
							for prompt in prompts:
											if isinstance(lowerCAmelCase__ ,  (tuple, list)		):
															__lowercase					     =				prompt[0]
															__lowercase					     =				float(prompt[1]		)
											elif ":" in prompt:
															__lowercase     ,	__lowercase					     =				prompt.split(''':'''		)
															__lowercase					     =				float(lowerCAmelCase__		)
											else:
															__lowercase					     =				prompt
															__lowercase					     =				1.0
											processed_prompts.append(lowerCAmelCase__		)
											weights.append(lowerCAmelCase__		)
							return {
							    "prompts": processed_prompts,
							    "weights": torch.tensor(lowerCAmelCase__ ,  device=self.device		),
							}





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=True ,  lowerCAmelCase__=False ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__=None ,  )	->   int:

							'''simple docstring'''


							if image_path:
											__lowercase					     =				self._get_latent(lowerCAmelCase__		)
							else:
											__lowercase					     =				torch.randn(self.latent_dim ,  device=self.device		)
							if self.log:
											self._init_logging(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							assert pos_prompts, "You must provide at least one positive prompt."
							__lowercase					     =				self.process_prompts(lowerCAmelCase__		)
							__lowercase					     =				self.process_prompts(lowerCAmelCase__		)
							if save_final and save_path is None:
											__lowercase					     =				os.path.join('''./outputs/''' ,  '''_'''.join(pos_prompts['''prompts''']		)		)
							if not os.path.exists(lowerCAmelCase__		):
											os.makedirs(lowerCAmelCase__		)
							else:
											__lowercase					     =				save_path + '''_''' + get_timestamp()
											os.makedirs(lowerCAmelCase__		)
							__lowercase					     =				save_path

							__lowercase					     =				self.vqgan.decode(self.latent		)[0]
							if show_intermediate:
											print('''Original Image'''		)
											show_pil(custom_to_pil(lowerCAmelCase__		)		)

							__lowercase					     =				loop_post_process(lowerCAmelCase__		)
							for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)		):
											if show_intermediate:
															show_pil(lowerCAmelCase__		)
											if save_intermediate:
															transformed_img.save(os.path.join(self.save_path ,  F"iter_{iter:03d}.png"		)		)
											if self.log:
															wandb.log({'''Image''': wandb.Image(lowerCAmelCase__		)}		)
							if show_final:
											show_pil(lowerCAmelCase__		)
							if save_final:
											transformed_img.save(os.path.join(self.save_path ,  F"iter_{iter:03d}_final.png"		)		)
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    torch.device("""cpu""")





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for k in state_dict.keys():
								__lowercase					     =				k
								if ".pwconv" in k:
												__lowercase					     =				k_new.replace('''.pwconv'''				,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												__lowercase					     =				k_new.replace('''.dwconv'''				,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												__lowercase					     =				k_new.replace('''.Proj.'''				,     '''.proj.'''				)
								if "patch_embed" in k_new:
												__lowercase					     =				k_new.replace('''patch_embed'''				,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												__lowercase					     =				k_new.split('''.'''				)
												if ls[2].isdigit():
																__lowercase					     =				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																__lowercase					     =				k_new.replace('''network'''				,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__lowercase					     =				1000
				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				'''imagenet-1k-id2label.json'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__lowercase					     =				[3, 3, 6, 4]
								__lowercase					     =				[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								__lowercase					     =				[3, 3, 9, 6]
								__lowercase					     =				[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								__lowercase					     =				[4, 3, 10, 5]
								__lowercase					     =				[48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								__lowercase					     =				[4, 4, 12, 6]
								__lowercase					     =				[64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												__lowercase					     =				torch.hub.load_state_dict_from_url(lowercase				,     map_location='''cpu'''				,     check_hash=lowercase				)
								else:
												__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				__lowercase					     =				checkpoint

				__lowercase					     =				create_rename_keys(lowercase				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)

				# load HuggingFace model
				__lowercase					     =				SwiftFormerForImageClassification(lowercase				).eval()
				hf_model.load_state_dict(lowercase				)

				# prepare test inputs
				__lowercase					     =				prepare_img()
				__lowercase					     =				ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				__lowercase					     =				processor(images=lowercase				,     return_tensors='''pt'''				)

				# compare outputs from both models
				__lowercase					     =				get_expected_output(lowercase				)
				__lowercase					     =				hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]				,     lowercase				,     atol=1E-3				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"				)
				hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a    :	Optional[int]         =    parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)